{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 55,
   "source": [
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "from matplotlib import cm\n",
    "import  matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import mpl_toolkits.mplot3d\n",
    "import re\n",
    "##读取文件\n",
    "filename = \"res_data.txt\"\n",
    "f = open(filename)\n",
    "list = f.readlines()\n",
    "x = np.zeros((len(list)-1))\n",
    "y = np.zeros((len(list)-1))\n",
    "z = np.zeros((len(list)-1))"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "source": [
    "line = re.findall(r\"\\d+\\.?\\d*\",list[0])\n",
    "nx = int(line[0]) + 1\n",
    "ny = int(line[1]) + 1\n",
    "for i in range(len(list)-1):\n",
    "    line = list[i+1].split() \n",
    "    x[i] = float(line[0])\n",
    "    y[i] = float(line[1])\n",
    "#    z[i] = float(line[2])\n",
    "    z[i] = float(line[2]) + (x[i]*x[i] + y[i] *y[i]) /4\n",
    "#    z[i] = np.cos(x[i]-y[i]) - float(line[2])"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "source": [
    "print(z)"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[ 0.00000000e+00 -3.38361593e-07 -1.83709326e-08 -4.13212199e-08\n",
      " -1.62704235e-07  3.21500503e-07  2.11804521e-07  1.63452858e-07\n",
      " -3.38361593e-07  3.34319000e-01  4.81675380e-01  5.19869982e-01\n",
      "  4.72927959e-01  3.55655337e-01  1.84891432e-01  2.11804521e-07\n",
      " -1.83709326e-08  4.81675380e-01  7.48128000e-01  8.45378662e-01\n",
      "  8.00779522e-01  6.31314015e-01  3.55655337e-01  3.21500503e-07\n",
      " -4.13212199e-08  5.19869982e-01  8.45378662e-01  9.93777090e-01\n",
      "  9.76459762e-01  8.00779522e-01  4.72927959e-01 -1.62704235e-07\n",
      " -1.62704235e-07  4.72927959e-01  8.00779522e-01  9.76459762e-01\n",
      "  9.93777090e-01  8.45378662e-01  5.19869982e-01 -4.13212199e-08\n",
      "  3.21500503e-07  3.55655337e-01  6.31314015e-01  8.00779522e-01\n",
      "  8.45378662e-01  7.48128000e-01  4.81675380e-01 -1.83709326e-08\n",
      "  2.11804521e-07  1.84891432e-01  3.55655337e-01  4.72927959e-01\n",
      "  5.19869982e-01  4.81675380e-01  3.34319000e-01 -3.38361593e-07\n",
      "  1.63452858e-07  2.11804521e-07  3.21500503e-07 -1.62704235e-07\n",
      " -4.13212199e-08 -1.83709326e-08 -3.38361593e-07  0.00000000e+00]\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "source": [
    "figure = plt.figure()\n",
    "ax=figure.gca(projection=\"3d\")\n",
    "x = x.reshape(nx,ny)\n",
    "y = y.reshape(nx,ny)\n",
    "z = z.reshape(nx,ny)\n",
    "ax.plot_surface(x,y,z,cmap=\"rainbow\")\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [],
   "outputs": [],
   "metadata": {}
  }
 ],
 "metadata": {
  "orig_nbformat": 4,
  "language_info": {
   "name": "python",
   "version": "3.7.10",
   "mimetype": "text/x-python",
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "pygments_lexer": "ipython3",
   "nbconvert_exporter": "python",
   "file_extension": ".py"
  },
  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3.7.10 64-bit ('base': conda)"
  },
  "interpreter": {
   "hash": "2c21737055494eda6dd7820cee4b5069ee6eef9e680dd0a23b71fc1db8cdc626"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}